Shaping the digital twin for design and production engineering

By Benjamin Rafael Schleich, Nabil Anwer, Luc Mathieu, Sandro Wartzack. 2017

1,085 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2b1987f89e2d25a5c0b6e89a01fd9a316d38bb25

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In Automotive Engineering

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Citations in the corpus

Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment (2021, background): “The following research questions are put forward for investigation: Azure Digital Twins Develop an autonomous resilient response to breakdown and failures in systems or processes [85]; response to unexpected incidents before occurring [90] Address network gaps in the ﬁeld and underground sites”
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, background): “In addition, the simulation capabilities related to the DT concept are very useful for planning and projecting products [33], [18].”
Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018, neutral): “[31].”
The Road Towards 6G: A Comprehensive Survey (2021, background): “and aviation, which have been developing and commercializing the digital twin in order to optimize their processes since last years [151].”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, methodology): “The Digital Twin can be learning and monitoring simultaneously, as well as applying machine and deep learning algorithms [33] [34] [35] [36].”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “, product design [12,13], modeling and simulation [14], fault diagnostics and prognostics [15].”
Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings (2019, methodology): “Existing buildings can therefore benefit from the implementation of a digital twin, which is a known concept in the field of manufacturing [22], for the”
Digital Twin in Industry: State-of-the-Art (2019, background): “They argued that the DT enabled designers to evaluate the quality of a product even at the early stage [36].”
Characterising the Digital Twin: A systematic literature review (2020, background): “5 Fidelity [33] [80] [108] [24] [85] [8] [56] [88] [68] [112] [99] [78] [32] [111] [36] [7] [71] [63] [61] [73] [20] [67] [6] [4] [48] [55] [95] [16] [92] [98] [87] [9] [66] [110] [18] [89] [12]”
Predictive maintenance using digital twins: A systematic literature review (2022, background): “One of the capabilities of the Digital Twin, according to [6], is to estimate the physical system’s response to an unexpected event before it occurs.”
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues (2020, methodology): “A Digital Twin is a high-fidelity representation of the operational dynamics of its physical counterpart, enabled by near real-time synchronization between the cyberspace and physical space [10].”
Deep learning in drug discovery: an integrative review and future challenges (2022, background): “The idea of making a "twin" of a process or a product returned to NASA’s Apollo project in the late 1960s (Rosen et al. 2015; Mayani et al. 2018; Schleich et al. 2017), when it assembled two identical space spacecraft.”
Integrated digital twin and blockchain framework to support accountable information sharing in construction projects (2021, background): “A digital twin is a virtual replica of physical assets, process, system or service which represents the properties (e.g., the geometry of assets), the condition (e.g., resource status), and the project ’ s performance [9 – 11].”